An adaptive ai-driven platform for data enrichment, stigma prediction, and multi-channel lead nurturing in audiology and related healthcare fields

Abstract

An Al-driven system and method for healthcare lead nurturing are disclosed. A system includes a data enrichment module configured to integrate internal and external data sources to enhance customer profiles, and a stigma prediction engine employing machine learning algorithms to analyze behavioral patterns and predict stigma levels. The system further includes a multi-channel communication module for delivering personalized content via email, SMS, chat, and voice calls. The system further includes an Al audiologist virtual assistant for real-time interactions, configured to adapt dynamically based on user input.

Description

AN ADAPTIVE AI-DRIVEN PLATFORM FOR DATA ENRICHMENT, STIGMA PREDICTION, AND MULTI-CHANNEL LEAD NURTURING IN AUDIOLOGY AND RELATED HEALTHCARE FIELDSTECHNICAL FIELD

[0001] The present invention relates to the field of healthcare technology, specifically to artificial intelligence (Al)-driven systems designed for lead generation, customer nurturing, and stigma reduction. While primarily focused on audiology, the system is adaptable to other healthcare sectors where patient engagement and stigma present significant challenges.BACKGROUND

[0002] Hearing loss is a pervasive global health concern affecting billions of individuals worldwide. Despite the availability of effective interventions such as hearing aids, the social stigma associated with their use significantly delays treatment — by an average of seven years. This delay exacerbates hearing loss and contributes to cognitive decline (and dementia), social isolation, and a diminished quality of life.

[0003] Existing solutions predominantly address hearing loss detection and diagnosis but lack robust mechanisms for personalized customer engagement and stigma mitigation. Current systems do not sufficiently guide individuals through the journey from awareness to action, resulting in low conversion rates and prolonged untreated hearing loss. Therefore, there is a critical need for an integrated platform that not only identifies potential hearing issues but also effectively engages and nurtures customers to overcome stigma and prompt timely intervention.SUMMARY

[0004] The present invention is an Al-powered platform designed to transform lead generation, customer nurturing, and stigma reduction in the audiology sector and beyond. The system leverages advanced machine learning and data enrichment to provide personalized, multichannel communication strategies that guide customers through their healthcare journey.

[0005] In one aspect, an Al-driven system for healthcare lead nurturing is disclosed. The system includes a data enrichment module configured to integrate internal and external data sources to enhance customer profiles, and a stigma prediction engine employing machine learning algorithms to analyze behavioral patterns and predict stigma levels. The system further includes a multi-channel communication module for delivering personalized content via email, SMS, chat, and voice calls. The system further includes an Al audiologist virtual assistant for real-time interactions, configured to adapt dynamically based on user input.

[0006] In another aspect, a method for Al-driven healthcare lead nurturing includes steps of collecting data from internal and external sources, enriching customer profiles using API integration and / or manual methods, predicting stigma levels via machine learning analysis of behavioral data, and generating personalized recommendations and content. The method can further include steps of delivering communications through multiple channels, facilitating interactions via an Al audiologist, and refining models based on feedback and interactions.

[0007] Implementations of the current subject matter can include, but are not limited to, methods consistent with the descriptions provided herein as well as articles that comprise a tangibly embodied machine-readable medium operable to cause one or more machines (e.g., computers, etc.) to result in operations implementing one or more of the described features.Similarly, computer systems are also described that may include one or more processors and one or more memories coupled to the one or more processors.

[0008] A memory, which can include a non-transitory computer-readable or machine- readable storage medium, may include, encode, store, or the like one or more programs that cause one or more processors to perform one or more of the operations described herein. Computer implemented methods consistent with one or more implementations of the current subject matter can be implemented by one or more data processors residing in a single computing system or multiple computing systems. Such multiple computing systems can be connected and can exchange data and / or commands or other instructions or the like via one or more connections, including but not limited to a connection over a network (e.g. the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, or the like), via a direct connection between one or more of the multiple computing systems, etc.

[0009] The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims. While certain features of the currently disclosed subject matter are described for illustrative purposes in relation to an audiology system, it should be readily understood that such features are not intended to be limiting. The claims that follow this disclosure are intended to define the scope of the protected subject matter.DESCRIPTION OF DRAWINGS

[0010] The accompanying drawings, which are incorporated in and constitute a part of this specification, show certain aspects of the subject matter disclosed herein and, together with thedescription, help explain some of the principles associated with the disclosed implementations. In the drawings,

[0011] FIG. 1 shows a process flow diagram illustrating aspects of a method having one or more features consistent with implementations of the current subject matter; and

[0012] FIG. 2 is a functional block diagram of an artificial intelligence (Al) engine configured for handling healthcare issues of a user.

[0013] When practical, similar reference numbers denote similar structures, features, or elements.DETAILED DESCRIPTION

[0014] The present invention relates to the field of healthcare technology, specifically but not limited to audiology, and to artificial intelligence (Al)-driven systems designed for customer lead generation, customer nurturing, and stigma reduction. While primarily focused on audiology, the system described herein, and methods executed thereby, are adaptable to other healthcare sectors where patient engagement and stigma present significant challenges.

[0015] This document describes an Al-powered platform, system and methods to transform lead generation, customer nurturing, and stigma reduction in healthcare sectors and beyond. The system leverages advanced machine learning and data enrichment to provide personalized, multi-channel communication strategies that guide customers through their healthcare journey.

[0016] In some implementations of the subject matter described herein, the systems and methods can enhance incomplete customer profiles by integrating external data sources, such as demographic data, through secure API connections. In situations where API integration is notfeasible, the platform can employ manual data enrichment processes. This dual approach ensures that customer profiles are comprehensive, enabling more accurate analysis and personalized interactions. In preferred implementations, all data processing occurs on servers within the data’s region of origin, ensuring compliance with local data protection regulations like the General Data Protection Regulation (GDPR) in Europe and the Health Insurance Portability and Accountability Act (HIPAA) in the United States, as examples.

[0017] In some implementations, the system utilizes scientifically validated methods and adaptive machine learning models to predict and measure stigma levels. By analyzing behavioral patterns, such as customer interactions, response times, content engagement, and screening behaviors, the platform tailors its communication strategies to address individual hesitations. Dynamic messaging delivers personalized content aimed at reducing stigma and encouraging proactive health decisions, such as educational materials that address common misconceptions about hearing health.

[0018] In some implementations, the system and platform employ a flexible, multi-channel communication system to engage customers at different points in their healthcare journey. For instance, the platform can use various communication channels, including but not limited to email, SMS, Al-driven chat, and Al-driven voice calls, to ensure that customers receive the most relevant information at each stage. As an example, customers who have just completed a hearing test may receive an email summarizing their results with a call-to-action to book an appointment. Those further along in the process may receive personalized SMS reminders or additional educational resources.

[0019] The system can include an Al-powered virtual assistant. In an audiological example, the presently disclosed system can include an Al audiologist, which can be implementedas an Al-powered virtual assistant capable of natural voice and chat interactions, and trained with domain-specific knowledge developed alongside professional audiologists, the Al audiologist operates 24 hours - 7 days / week, providing customers with the flexibility to interact whenever they choose. The virtual assistant discusses hearing test results, answers frequently asked questions about hearing health, schedules appointments, and intelligently redirects complex inquiries by offering follow-up actions with human professionals. Additionally, the Al audiologist may be configured via an interactive portal or conversational setup process, and its behavior may dynamically adapt during active interactions based on real-time model outputs.

[0020] As a system based on machine learning, the present invention can continuously learn from customer interactions. The system’s adaptive algorithms refine predictive models using new data and interaction outcomes, enhancing stigma prediction accuracy and engagement effectiveness over time. The platform is not confined to specific machine learning models and can integrate emerging algorithms to stay at the forefront of Al technology. The platform integrates a self-learning AI / ML model that identifies patterns in hearing loss and customer behavior. It improves over time by learning from incomplete data and real-world interactions.

[0021] A recommendation engine forms the foundation of a Next Best Action (NBA) module, which provides personalized recommendations to guide customers through their healthcare journey efficiently. By analyzing customer actions, the system optimizes engagement strategies and adjusts nurturing processes based on individual responses and progression. The system analyzes data from both internal sources, including hearing screening kiosks and webbased screeners, and external datasets via secure APIs. The system processes information in real time to generate tailored recommendations, ensuring timely and relevant customer interactions. The platform can include, or integrate with, a digital marketplace offering products and servicesrelated to healthcare, such as, in an audiological example, tinnitus treatments, hearing aids, and educational resources on hearing health. This provides long-term engagement options, extending customer relationships beyond initial screenings and encouraging ongoing involvement in health management. The system is further extensible to retail environments, digital ecommerce platforms, and call center operations, where it can either guide human users in real time or operate autonomously.

[0022] Data Enrichment and Secure Data Handling

[0023] In preferred implementations, the systems and methods described herein, which make up the platform, enrich customer data by securely integrating external data sources through APIs with external providers or estimating results and performance, for example through imputation. This process fills gaps in customer profiles, enhancing demographic and behavioral insights essential for personalized engagement. When API integration is not feasible, the system employs manual data enrichment methods to ensure comprehensive customer profiles. All data processing occurs on servers within the data’s region of origin, complying with local data protection laws such as the GDPR in Europe and HIPAA in the United States. This ensures that customer data does not cross international borders without consent, providing an additional layer of security and privacy.

[0024] Stigma Prediction and Behavioral Analysis

[0025] The system utilizes adaptive machine learning algorithms trained on extensive datasets to predict stigma levels associated with healthcare interventions. By analyzing customer interactions, response times, content engagement, and screening behaviors, the platform adjusts communication strategies dynamically based on predicted stigma levels. Personalized content is delivered to overcome hesitation and encourage proactive health decisions. For example, if thesystem detects that a customer is hesitant due to stigma, it may send educational materials addressing common misconceptions about hearing aids or highlight success stories from other users.

[0026] Multi-Channel Nurturing Process

[0027] The present invention employs a flexible, multi-channel communication system to engage customers at various stages of their healthcare journey. Communication channels include but are not limited to email, SMS, chat, social media platforms, and Al-driven voice calls.• Email: send personalized summaries of hearing test results with educational information and clear calls-to-action, such as scheduling an appointment.• SMS: provide timely reminders, educational snippets, and appointment confirmations to keep customers engaged.• Chat and voice calls: engage customers through interactive conversations facilitated by the Al audiologist, offering immediate assistance and proactive educational information.

[0028] Messaging can be tailored based on customer data and behavioral insights, ensuring relevance and increasing the likelihood of engagement and conversion.

[0029] Al Audiologist for Dynamic Customer Interaction

[0030] In preferred implementations, the Al audiologist is an Al-powered virtual assistant capable of natural language communication via voice and chat interfaces. Developed in collaboration with professional (human) audiologists, it can be trained with domain-specific knowledge to provide accurate and relevant information. Operating 24 / 7, the Al audiologist offers customers the flexibility to interact at their convenience or it may proactively follow-up withcustomers with educational information and call-to-actions. It discusses hearing test results, answers frequently asked questions about hearing health, and schedules appointments directly within the system. If the Al encounters a complex question outside its scope, it may redirect the customer by suggesting follow-up actions with human professionals, ensuring a seamless user experience.

[0031] Configurable Al Audiologist via Portal or Automated Conversation

[0032] In certain implementations, the Al audiologist may be configured via a user- accessible configuration portal, enabling business users (e.g., audiology practices, retail clinics, enterprise partners) to define operational parameters of the Al system. These parameters may include, but are not limited to, custom dialogue scripts, escalation rules, tone and language style, content priority rules, and scheduling policies. The portal may include a graphical user interface or command-line interface and may be deployed as a standalone application or embedded within an administrative dashboard. The configuration data may be stored in a persistent state and linked to specific business accounts or campaigns.

[0033] In alternative implementations, the configuration process may be performed in an automated or semi-automated manner via interaction with the Al audiologist itself. A business user may initiate a configuration session through a chat interface or voice conversation with the agent, during which the system extracts intent, operational preferences, and tone settings from the spoken or typed input. In such implementations, the configuration may also optionally include voice modeling, wherein the Al audiologist records the user’s voice during the configuration process and creates a personalized voice synthesis model. This model can replicate the user’s vocal characteristics, such as pitch, accent, prosody, and timbre, for use in future outbound communications, thus maintaining brand alignment or personalization at scale.

[0034] In further implementations, the AT audiologist may operate in conjunction with one or more backend Al models that execute real-time inference during active customer interactions. As customer responses, behaviors, and feedback are received by the Al audiologist (whether via voice, chat, or other input modalities) such input can be transmitted to a real-time analytics engine. This engine evaluates the interaction context using pre-trained or dynamically updated models, and returns updated response guidance to the Al agent. The Al audiologist may then adjust its communication approach mid-session, including but not limited to altering tone, skipping or reordering steps in a dialogue tree, modifying recommendations, or initiating escalation protocols. This closed-loop feedback system enables adaptive, context-sensitive interactions and ensures that the Al’s behavior remains responsive to customer needs, sentiment, and progression through the care journey.

[0035] Exemplary methods for configuring an Al-powered voice agent include the following steps and variations thereof: receiving configuration input via a web-based portal or interactive dialogue session; storing configuration parameters linked to a business user account; and optionally generating a personalized speech model based on a recorded voice sample during said configuration interaction. Exemplary methods for dynamically adapting conversational behavior of an Al-driven virtual agent during a user interaction include the following steps and variations thereof:• receiving real-time user input via a communication interface, the input comprising at least one of voice data, text data, or behavioral signals;• transmitting the user input to one or more Al models configured to generate inference outputs indicative of user state, sentiment, or intent;• receiving the inference outputs from said models during the ongoing interaction;• selecting or modifying a subsequent conversational response based on the received inference outputs; and• delivering the modified conversational response to the user via the communication interface, wherein the conversational behavior can be continuously adjusted throughout the interaction session in accordance with updated inference outputs.

[0036] Continuous Learning and Adaptation

[0037] The system’s adaptive algorithms continuously refine predictive models using ongoing interaction data and feedback. This enhances stigma prediction accuracy and engagement effectiveness over time. By incorporating real-time customer feedback, the platform adjusts its strategies to better meet individual needs. A / B testing may be utilized to determine the most effective communication approaches, allowing the system to learn which messages and channels yield the highest engagement rates. The platform is not confined to any specific machine learning models and can be configured to be able to integrate new algorithms to enhance performance, ensuring that it remains at the forefront of Al-driven customer engagement technology.

[0038] Integration of Educational and Behavioral Models

[0039] The present invention integrates a self-learning AI / ML model that identifies patterns in hearing loss, customer behavior, stigma, and other relevant (hearing) health factors. The model continuously improves by learning from incomplete data and real-world interactions, ensuring that the system remains accurate and relevant as new information becomes available. The platform’s recommendation engine forms the foundation of its Next Best Action Module, which uses behavioral analytics to guide customers through their healthcare journey efficiently. By analyzing customer actions, the system can be configured to provide personalizedrecommendations, helping individuals move through awareness, preparation, and action phases more effectively. The system continuously refines its recommendations based on real-time feedback, ensuring that its nurturing strategies remain relevant and effective.

[0040] Internal and External Data Source Analysis

[0041] The platform collects and processes data from internal sources, such as hearing screening kiosks and web-based screeners, utilizing proprietary tools to gather actionable insights. It also accesses third-party datasets via secure APIs to enhance predictive models and personalization capabilities. Data can be processed in real time, allowing the system to provide immediate recommendations and ensure timely and relevant customer interactions. This real-time processing is crucial for engaging customers when they are most receptive, thereby increasing the likelihood of positive outcomes.

[0042] Digital Marketplace Integration

[0043] Some implementations of the invention disclosed herein may include or integrate with a digital marketplace that offers products and services related to hearing health, such as tinnitus treatments, hearing aids, and educational resources. This integration provides long-term engagement opportunities, extending customer relationships beyond initial screenings and encouraging ongoing involvement in health management. By offering a comprehensive suite of resources, the platform ensures that customers have access to the tools and information they need to manage their hearing health effectively.

[0044] In-Store Al Coaching and Fully Digital Ecommerce Assistant

[0045] In some implementations, the system can be configured to operate in an in-store environment to provide real-time coaching and sales enablement for human employees during livecustomer interactions. An Al system can monitor or receive live input on the progress of a consultation (via direct integrations with store systems or via employee input) and generate personalized, step-by-step coaching guidance. Such guidance can include, without limitation, recommended questions to ask, answers to common objections, and calls-to-action, optimized for achieving predefined goals such as increased conversion rates, better customer satisfaction, or shorter interaction time.

[0046] The system can ensure data-driven consistency across all store locations, eliminating variability due to employee experience, memory, or training level. In this setup, retail staff may require little to no formal training: all key interaction components (e.g., recommendations, phrasing, objection handling) can be scripted, guided, and continuously optimized by Al, ensuring high performance across staff and stores at scale. Business users may configure desired objectives (e.g., maximize appointments, minimize consultation time, upsell particular SKUs), which an Al system can then use to adapt its coaching scripts dynamically.

[0047] In other implementations, the system operates as a fully digital assistant integrated into a webshop, customer portal, or landing page. This digital Al agent guides customers through an end-to-end journey, from initial interest to education, product discovery, lead qualification, and conversion, entirely online. The digital journey may mimic the structure of in-person coaching, but can be optimized for asynchronous or autonomous interaction. This feature enables brands to operate fully self-service ecommerce flows, supported by real-time conversational guidance, personalized recommendations, and behavioral “nudging,” or guiding, driven by backend Al models.

[0048] Whether in-store or fully digital, in various implementations the system can be architected to integrate seamlessly into existing customer-facing workflows. The Al modules mayinterface with ads, landing pages, call center operations, EHRs / CRMs, and store systems via a modular API toolkit (as described in accompanying figures). This ensures that organizations can adopt the system’s activation and conversion stack without disrupting their current retail, call center, or ecommerce environments.

[0049] Exemplary systems for guiding retail employees or digital users through a sales journey may include one or more of the following: a front-end interface configured to deliver realtime interaction guidance; a backend model configured to optimize prompts based on organizational objectives and customer behavior; and an integration layer enabling deployment in physical retail locations and digital ecommerce environments, wherein the system ensures consistent, high-performance customer engagement without requiring staff training.

[0050] Call Center Integration and Business User Labeling of Consumers

[0051] In certain use cases, the system can be further adapted to support customer service or call center agents and business users, including but not limited to enterprise-level organizations, small and medium-sized enterprises (SMEs), and independent retail operators such as audiology clinics or single-location stores, who engage with leads on behalf of business clients. In this implementation, business users may access a dedicated interface through which they can label, classify, or annotate consumer leads following a telephonic or digital interaction. Labels may include conversion intent, readiness level, sentiment, qualification tier, or other custom tags. This agent-facing interface may display Al-generated predictions (e.g., likelihood to convert, predicted stigma level, recommended next contact time), while also allowing the human agent to override or enrich these classifications based on their subjective assessment. The updated labels may be stored in the system and used to further train the Al models through supervised learning.

[0052] Exemplary systems for lead management or business users may include one or more of: a call center interface for viewing and interacting with consumer leads; a classification module enabling human agents to label consumer profiles; and a training module configured to incorporate said labels into an adaptive machine learning model.

[0053] Referring to FIG. 1, an exemplary flowchart illustrates an operational workflow of a system and method described herein, and specifically detailing sequential processes and interactions between core components of a system. The process initiates with data collection from two primary sources. Block 101 represents the internal data sources, where customer information is gathered from hearing screening kiosks and web-based hearing screeners. Block 102 denotes the external data sources, incorporating third-party datasets accessed via secure APIs from providers like Acxiom ™.

[0054] The collected data proceeds to the data enrichment module (block 103). In some processes, the system enhances incomplete customer profiles by integrating the internal and / or external data, and through smart prediction of results. Secure API integration can be utilized for automated data enrichment, and when API integration is unavailable, manual data enrichment methods are employed. This ensures comprehensive customer profiles for more accurate analysis.

[0055] The enriched data is then stored and processed within the data storage unit (block 104). Here, the system ensures that all data handling complies with regional data protection regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). Data can be processed on servers located within the region of origin to maintain compliance and enhance security.

[0056] Next, the enriched data is analyzed by the stigma prediction engine (block 105). This engine employs adaptive machine learning algorithms to predict individual stigma levelsassociated with healthcare interventions. By analyzing customer interactions, response times, content engagement, and screening behaviors, the system generates predictive insights into each customer’s potential hesitations. These insights feed into the Personalization and recommendation engine (block 106), which generates personalized content and determines the most effective communication strategies.

[0057] The engine can be configured to tailor messages and recommendations based on the predicted stigma levels and enriched customer profiles to encourage proactive health decisions. The personalized communication plans are executed through the multi-channel communication module (block 107). This module delivers messages via various channels, including email, SMS, Al-driven chat interfaces, and Al-driven voice calls. It schedules communications based on customer preferences and behaviors to maximize engagement.

[0058] Customers interact with the system through these channels, engaging with the Al audiologist interaction (block 108). The Al audiologist is an Al-powered virtual assistant capable of natural language communication via voice and chat. It discusses hearing test results, answers frequently asked questions, schedules appointments, and intelligently redirects complex inquiries to human professionals when necessary. The outcomes of these interactions are captured in customer actions (block 109). This block represents the various actions taken by customers, such as booking appointments, accessing educational resources, or providing feedback on services and interactions.

[0059] Customer feedback and engagement metrics flow into the feedback loop and continuous learning module (block 110). This module continuously refines the machine learning models and personalization strategies based on ongoing interaction data. By incorporating real-time feedback, the system enhances its predictive capabilities and adjusts engagement approaches to improve effectiveness over time.

[0060] Accordingly, the systems and methods described herein provide a transformative approach to lead generation and customer engagement in the audiology industry and other healthcare sectors. By integrating advanced Al technologies with personalized communication strategies, the platform effectively addresses the challenges of stigma and delayed treatment adoption. The system’s adaptive architecture ensures continuous improvement and scalability, making it a robust solution for businesses seeking to enhance customer conversion and retention. Its compliance with data privacy regulations and ability to process data securely within regional boundaries further strengthen its applicability in diverse markets. The platform’s extensibility to in-store coaching, fully digital ecommerce journeys, and enterprise call center integration further enhances its versatility and commercial applicability.

[0061] While described herein in the context of audiology, the platform’s methodologies are adaptable to other healthcare fields where stigma and patient engagement are barriers to treatment, such as mental health services, chronic disease management, and preventive care programs. The methods employed by the systems described herein can be tailored to address specific challenges in these areas, making it a versatile tool for improving patient outcomes across various healthcare domains. The system can be applicable across the healthcare technology industry, including, but not limited to:• Healthcare providers: enhancing patient engagement and treatment adoption rates by providing personalized communication and reducing barriers to care.• Medical device manufacturers: improving lead nurturing and customer education through targeted messaging and Al-driven interactions.• Patient engagement platforms: offering personalized, Al-driven solutions for customer acquisition and retention, leading to better health outcomes and increased satisfaction.• Retail organizations: enabling consistent, high-performance in-store engagement without requiring employee training.• Ecommerce platforms: providing end-to-end digital guidance, education, and conversion through Al-driven conversational agents.• Call centers and CRM platforms: supporting agents with Al-assisted lead scoring, customer labeling, and consistent follow-up workflows

[0062] FIG. 2 illustrates a system and method for an Al system to process and handle a healthcare situation of a user. The user can interact with an Al engine with inbound calls, which can be voice calls, email, text messages, direct messages, or the like. The Al engine is configured to receive the inbound call(s), parse and process any language discerned from the call, and generate one or more actions. As an example, and in the context of an Al audiology system, the Al engine can generate one or more of the following actions: books any sort of appointment (block 202), including hearing tests, service appointments, etc.; answers general questions (block 204) about hearing aids or pricing; helps resolve simple issues (block 206) about a hearing aid or other device; lets the caller leave a message for a human (block 208); and perform initial triage (block 210).

[0063] The Al system and engine can also execute outbound calls, such as voice calls, messaging, or the like, to: contact leads to collect info on the user’s situation (block 222) such as their hearing situation; ask guided screening questions (block 224); capture preferences (interest in appointment, lifestyle, device usage, etc.) at block 226; hands off to a human scheduler, or booksdirectly if integrated (block 228); and / or if not ready, offers to email the user a link to a webscreener or other web-based screening tool.

[0064] In some implementations, the system and Al engine is configured to contact leads, who have shown a certain form of engagement, for example took a hearing test, but did not do something afterwards, to stimulate the user to come in again for a physical visit. The Al engine can further be configured to manage check-in on a hearing aid trial, schedule follow-ups on appointments, and provide re-scheduling of appointments (also for inbound calls).

[0065] These above actions, whether inbound our outbound, can occur with or without including the stigma prediction and conversion probability scoring. Accordingly, the agent, i.e., Al engine, is not really intended to be medical doctor, but acts as a knowledgeable hearing care assistant that understands the clinic workflow.

[0066] A voice agent component of the Al engine can be configured to work as a companion for both employees in the field, as well as for consumers. In the former case, the agent works closely with the practice manager / clinic owner, to analyze, discuss, resolve client specific questions and issues. In the latter case, the Al engine is able to guide consumers in their journey, which could mean recommending a specific clinic / clinician / hearing aid, etc.

[0067] One or more aspects or features of the subject matter described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) computer hardware, firmware, software, and / or combinations thereof. These various aspects or features can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data andinstructions to, a storage system, at least one input device, and at least one output device. The programmable system or computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0068] These computer programs, which can also be referred to programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural language, an object- oriented programming language, a functional programming language, a logical programming language, and / or in assembly / machine language. As used herein, the term “machine-readable medium” refers to any computer program product, apparatus and / or device, such as for example magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs), used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine- readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor. The machine-readable medium can store such machine instructions non- transitorily, such as for example as would a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium. The machine-readable medium can alternatively or additionally store such machine instructions in a transient manner, such as for example as would a processor cache or other random access memory associated with one or more physical processor cores.

[0069] To provide for interaction with a user, one or more aspects or features of the subject matter described herein can be implemented on a computer having a display device, such as forexample a cathode ray tube (CRT) or a liquid crystal display (LCD) or a light emitting diode (LED) monitor for displaying information to the user and a keyboard and a pointing device, such as for example a mouse or a trackball, by which the user may provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well. For example, feedback provided to the user can be any form of sensory feedback, such as for example visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including, but not limited to, acoustic, speech, or tactile input. Other possible input devices include, but are not limited to, touch screens or other touch- sensitive devices such as single or multi-point resistive or capacitive trackpads, voice recognition hardware and software, optical scanners, optical pointers, digital image capture devices and associated interpretation software, and the like.

[0070] In the descriptions above and in the claims, phrases such as “at least one of’ or “one or more of’ may occur followed by a conjunctive list of elements or features. The term “and / or” may also occur in a list of two or more elements or features. Unless otherwise implicitly or explicitly contradicted by the context in which it used, such a phrase is intended to mean any of the listed elements or features individually or any of the recited elements or features in combination with any of the other recited elements or features. For example, the phrases “at least one of A and B;” “one or more of A and B;” and “A and / or B” are each intended to mean “A alone, B alone, or A and B together.” A similar interpretation is also intended for lists including three or more items. For example, the phrases “at least one of A, B, and C;” “one or more of A, B, and C;” and “A, B, and / or C” are each intended to mean “A alone, B alone, C alone, A and B together, A and C together, B and C together, or A and B and C together.” Use of the term “based on,” above and inthe claims is intended to mean, “based at least in part on,” such that an unrecited feature or element is also permissible.

[0071] The subject matter described herein can be embodied in systems, apparatus, methods, and / or articles depending on the desired configuration. The implementations set forth in the foregoing description do not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the described subject matter. Although a few variations have been described in detail above, other modifications or additions are possible. In particular, further features and / or variations can be provided in addition to those set forth herein. For example, the implementations described above can be directed to various combinations and subcombinations of the disclosed features and / or combinations and subcombinations of several further features disclosed above. In addition, the logic flows depicted in the accompanying figures and / or described herein do not necessarily require the particular order shown, or sequential order, to achieve desirable results. Other implementations may be within the scope of the following claims.

Claims

CLAIMSWhat is claimed is:

1. An Al-driven system for healthcare lead nurturing, comprising: a data enrichment module configured to integrate internal and external data sources to enhance customer profiles; a stigma prediction engine employing machine learning algorithms to analyze behavioral patterns and predict stigma levels; a multi-channel communication module for delivering personalized content via email, SMS, chat, and voice calls; and an Al audiologist virtual assistant for real-time interactions, configured to adapt dynamically based on user input.

2. The system of claim 1, wherein the data enrichment module uses secure APIs for automated integration and manual processes when APIs are unavailable, with all processing occurring on regional servers to comply with data protection regulations.

3. The system of claim 1, wherein the stigma prediction engine analyzes customer interactions, response times, content engagement, and screening behaviors to tailor communication strategies.

4. The system of claim 1, wherein the multi-channel communication module includes personalized emails with test summaries, SMS reminders, and Al-driven chats for proactive engagement.

5. The system of claim 1, wherein the Al audiologist is configurable via a portal or conversational setup, including voice modeling for personalized synthesis.

6. The system of claim 1, further comprising a continuous learning module that refines models using interaction data and AZB testing for optimized engagement.

7. The system of claim 1, further comprising a recommendation engine and next best action module for guiding customers through healthcare journeys based on behavioral analytics.

8. The system of claim 1, integrated with a digital marketplace for offering healthcare products and services.

9. The system of claim 1, extensible to in-store Al coaching for real-time employee guidance and fully digital ecommerce assistants.

10. The system of claim 1, further including call center integration with interfaces for labeling consumer leads and training Al models.

11. A method for Al-driven healthcare lead nurturing, comprising: collecting data from internal and external sources; enriching customer profdes using API integration and / or manual methods; predicting stigma levels via machine learning analysis of behavioral data; generating personalized recommendations and content; delivering communications through multiple channels; facilitating interactions via an Al audiologist; and refining models based on feedback and interactions.

12. The method of claim 11, wherein data processing complies with regional regulations such as GDPR and HIPAA.

13. The method of claim 11, wherein predicting stigma levels includes analyzing interactions, response times, and engagement metrics.

14. The method of claim 11, wherein delivering communications includes tailoring messages based on predicted stigma and customer progression.

15. The method of claim 11, further comprising configuring an Al audiologist module via a portal or automated conversation, with dynamic adaptation during interactions.

16. The method of claim 11, further comprising integrating with retail, e- commerce, and call center environments.

17. The method of claim 11, wherein refining models includes A / B testing and incorporating real-time feedback.

18. The method of claim 11, further comprising providing in-store coaching or digital guidance to optimize customer conversions without staff training.

19. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method comprising: collect data from internal and external sources; enrich customer profiles using API integration and / or manual methods; predict stigma levels via machine learning analysis of behavioral data; generate personalized recommendations and content; deliver communications through multiple channels; facilitate interactions via an Al audiologist; and refine models based on feedback and interactions.

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